Daniel Precioso GarcelánUniversidad de Cádiz | UCA · Department of Computer Engineering
Daniel Precioso Garcelán
PhD student on Machine Learning
Working on my Phd: Applications of Machine Learning and Data Science to Blue Economy
About
14
Publications
2,394
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10
Citations
Introduction
I am a PhD student at University of Cadiz, focused on the many applications of Machine Learning to industry 4.0. My wish is to aid employees with task-specific AIs that assist them in otherwise repetitive and time-consuming task. I also aim for using my data science skills to extract relevant information from the industry data. By the proper use of these data we can reduce the spend of resources, mitigate the environmental impact and avoid the generation of excessive waste.
Additional affiliations
September 2019 - October 2020
Position
- PhD Student
Description
- Studying a PhD on Machine Learning. Member of UCA Datalab, a meeting point for Machine Learning, Computer Vision, IoT, Robotics and Cybersecurity developments. "The swiss knife of data science projects". http://datalab.uca.es/
Education
September 2018 - July 2019
September 2014 - July 2018
Publications
Publications (14)
In this paper, we present a novel algorithm called the Hybrid Search algorithm that integrates the Zermelo's Navigation Initial Value Problem with the Ferraro-Mart\'in de Diego-Almagro algorithm to find the optimal route for a vessel to reach its destination. Our algorithm is designed to work in both Euclidean and spherical spaces and utilizes a he...
Based on data gathered by echo-sounder buoys attached to drifting fish-aggregating devices (dFADs) across tropical oceans, we applied a machine learning protocol to examine the temporal trends of tuna-school associations with drifting objects both in comparison to previous studies, and in the context of the ‘ecological trap’ theory. Using a binary...
Background
We estimated the association between the level of restriction in nine different fields of activity and SARS-CoV-2 transmissibility in Spain, from 15 September 2020 to 9 May 2021.
Methods
A stringency index (0–1) was created for each Spanish province ( n = 50) daily. A hierarchical multiplicative model was fitted. The median of coefficie...
Non-intrusive load monitoring (NILM) is the problem of predicting the status or consumption of individual domestic appliances only from the knowledge of the aggregated power load. NILM is often formulated as a classification (ON/OFF) problem for each device. However, the training datasets gathered by smart meters do not contain these labels, but on...
In this work we introduce
NeoCam
, an open source hardware-software platform for video-based monitoring of preterms infants in Neonatal Intensive Care Units (NICUs).
NeoCam
includes an edge computing device that performs video acquisition and processing in real-time. Compared to other proposed solutions, it has the advantage of handling data mo...
Non-Intrusive Load Monitoring (NILM) aims to predict the status or consumption of domestic appliances in a household only by knowing the aggregated power load. NILM can be formulated as regression problem or most often as a classification problem. Most datasets gathered by smart meters allow to define naturally a regression problem, but the corresp...
Based on the data gathered by echo-sounder buoys attached to drifting Fish Aggregating Devices (dFADs) across tropical oceans, the current study applies a Machine Learning protocol to examine the temporal trends of tuna schools' association to drifting objects. Using a binary output, metrics typically used in the literature were adapted to account...
Based on the data gathered by echo-sounder buoys attached to drifting Fish Aggregating Devices (dFADs) across tropical oceans, the current study applies a Machine Learning protocol to examine the temporal trends of tuna schools' association to drifting objects. Using a binary output, metrics typically used in the literature were adapted to account...
Background
We estimated the association between the level of restriction in nine different fields of activity and SARS-CoV-2 transmissibility in Spain, from 15 September 2020 to 9 May 2021.
Methods
A stringency index (0 to 1) was created for mobility, social distancing, commerce, indoor and outdoor bars and restaurants, culture and leisure, worshi...
The use of dFADs by tuna purse-seine fisheries is widespread across oceans, and the echo-sounder buoys attached to these dFADs provide fishermen with estimates of tuna biomass aggregated to them. This information has potential for gaining insight into tuna behaviour and abundance, but has traditionally been difficult to process and use. The current...
Echo-sounder data registered by buoys attached to drifting FADs provide a very valuablesource of information on populations of tuna and their behaviour. This value increases whenthese data are supplemented with oceanographic data coming from CMEMS. We use thesesources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomassunde...
Non-Intrusive Load Monitoring (NILM) is generally framed as a supervised learning problem whose input is the time series for aggregated power load of a household and whose output is the time series for the consumption of an individual appliance. Often the interest lies in predicting whether an appliance is ON or OFF, rather than its power usage. In...
Non-Intrusive Load Monitoring (NILM) aims to predict the status or consumption of domestic appliances in a household only by knowing the aggregated power load. NILM can be formulated as regression problem or most often as a classification problem. Most datasets gathered by smart meters allow to define naturally a regression problem, but the corresp...
Non-intrusive load monitoring (NILM) is a technique aimed to detect which appliances are turned on in a certain building, by only knowing the aggregated electric load. Currently, there is a special interest in NILM thanks to its multiple applications and its low cost implementation. Several state-of-the-art techniques are being applied in this disc...
